Magnetic resonance imaging has become established as a key technique for imaging the soft tissues of the body. MRI images are predominantly formed by the measurement of radio frequency signal emission during proton spin relaxation following an excitation signal to protons located in a magnetic field. The use of magnetic field gradients allows spatially encoded data to be acquired to form an image. The data are acquired in so-called “k-space”, related via Fourier transform to the physical space from where an image is acquired—different positions in “k space” correspond to spatial frequency and phase information. The task of forming an MRI image can be viewed as acquiring lines in k-space (“ky lines”) to span the entire k-space to be imaged, and then reconstructing the spatial image by Fourier transform.
The physics underlying magnetic resonance imaging relies on the relaxation time of protons (or, occasionally, the relaxation time of other NMR active nuclei), and so acquisition of sufficient data to form an image takes significant time in relation to expected movement of a subject to be imaged, such as a human body. This problem is particularly acute when imaging structures within the thorax of a subject, as they are subject to cyclic motion from the subject's breathing during a typical timescale for image acquisition. The problem is further exacerbated in the field of cardiac imaging, where the beating of the heart adds a second cyclic motion to the problem.
The problem has been addressed by the use of so-called ‘navigator acceptance’ imaging methods in which positional information is gathered effectively simultaneously with image data, but these have been hindered by the loss in scan efficiency which results from the changes in breathing pattern during a scan. The technique known as ‘phase ordering with automatic window selection’ (PAWS) provides a method which is resistant to changes in breathing whilst allowing the user the use of phase ordering to provide effective motion artefact reduction in an optimal time (Jhooti P, Gatehouse P D, Keegan J, Bunce N H, Taylor A M, Firmin D N. “Phase ordering with automatic window selection (PAWS): a novel motion-resistant technique for 3D coronary imaging”, Magnetic Resonance in Medicine, 2000, March, 43(3): 470-80.). The drawback of the PAWS technique is that images are only available once enough data has been acquired within the range of motion specified. Whilst the acquisition may terminate with the optimal scan time for the particular respiratory trace and acceptance window size, this optimal time may still be quite long.
Other techniques such as the Diminishing Variance Algorithm (DVA) acquire the whole image before attempting to limit the respiratory motion (Sachs T S, Meyer C H, Irarrazabal P, Hu B S, Nishimura D G, Macovski A., “The diminishing variance algorithm for real-time reduction of motion artefacts in MRI”, Magnetic Resonance in Medicine, 1995, 34:412-422). Whilst DVA has the advantage of allowing scans to terminate at any point after the initial image has been corrected, the algorithm has been found to be less effective in subjects with a variable respiratory pattern (Jhooti et al, ibid).
These navigator acceptance imaging methods are invariably compromised when the breathing pattern of a subject changes during the scan. Techniques such as PAWS have attempted to overcome such problems by the use of automatic sampling strategies which make no assumptions as to the final acceptance window. As all possible windows are treated with equal importance, the techniques have been shown to be effective in situations of changes of respiration whilst also allowing scans to terminate in an optimal time. However, as this technique focuses on its final optimal image, no image is available until this has been acquired.
The DVA algorithm attempts to provide an image as soon as possible, and then to continually improve this image until a particular range of motion is achieved, or scan quality is deemed to be satisfactory. However, as this techniques makes a decision as to which range of motion will be accepted, and further acquisitions made accordingly, the situation is unsuitable in situations of respiratory change. A technique is therefore required to overcome these disadvantages.
The present invention attempts to combine the noted benefits of the DVA and PAWS technique to provide a methodology that enables images to be reconstructed quickly, and with all further data acquisition reducing the acceptance window and improving image quality, whilst ensuring that a scan terminates automatically in an optimal scan time for a given acceptance window size regardless of respiratory pattern.